Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations8950
Missing cells21475
Missing cells (%)8.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory216.0 B

Variable types

Numeric15
Text5
Categorical3
DateTime4

Alerts

10 km Miejsce Open is highly overall correlated with 10 km Tempo and 9 other fieldsHigh correlation
10 km Tempo is highly overall correlated with 10 km Miejsce Open and 9 other fieldsHigh correlation
15 km Miejsce Open is highly overall correlated with 10 km Miejsce Open and 9 other fieldsHigh correlation
15 km Tempo is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
20 km Miejsce Open is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
20 km Tempo is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
5 km Miejsce Open is highly overall correlated with 10 km Miejsce Open and 9 other fieldsHigh correlation
5 km Tempo is highly overall correlated with 10 km Miejsce Open and 9 other fieldsHigh correlation
Kategoria wiekowa is highly overall correlated with Płeć and 1 other fieldsHigh correlation
Kategoria wiekowa Miejsce is highly overall correlated with 20 km Tempo and 1 other fieldsHigh correlation
Miejsce is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
Płeć is highly overall correlated with Kategoria wiekowa and 1 other fieldsHigh correlation
Płeć Miejsce is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
Rocznik is highly overall correlated with Kategoria wiekowaHigh correlation
Tempo is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
Tempo Stabilność is highly overall correlated with 15 km Tempo and 4 other fieldsHigh correlation
Kraj is highly imbalanced (94.2%) Imbalance
Miejsce has 800 (8.9%) missing values Missing
Miasto has 1089 (12.2%) missing values Missing
Kraj has 800 (8.9%) missing values Missing
Drużyna has 5529 (61.8%) missing values Missing
Płeć Miejsce has 800 (8.9%) missing values Missing
Kategoria wiekowa Miejsce has 809 (9.0%) missing values Missing
Rocznik has 201 (2.2%) missing values Missing
5 km Czas has 827 (9.2%) missing values Missing
5 km Miejsce Open has 827 (9.2%) missing values Missing
5 km Tempo has 827 (9.2%) missing values Missing
10 km Czas has 811 (9.1%) missing values Missing
10 km Miejsce Open has 811 (9.1%) missing values Missing
10 km Tempo has 834 (9.3%) missing values Missing
15 km Czas has 809 (9.0%) missing values Missing
15 km Miejsce Open has 809 (9.0%) missing values Missing
15 km Tempo has 814 (9.1%) missing values Missing
20 km Czas has 806 (9.0%) missing values Missing
20 km Miejsce Open has 806 (9.0%) missing values Missing
20 km Tempo has 813 (9.1%) missing values Missing
Tempo Stabilność has 840 (9.4%) missing values Missing
Tempo has 800 (8.9%) missing values Missing
Rocznik is highly skewed (γ1 = -27.2834571) Skewed
Miejsce is uniformly distributed Uniform
5 km Miejsce Open is uniformly distributed Uniform
10 km Miejsce Open is uniformly distributed Uniform
15 km Miejsce Open is uniformly distributed Uniform
20 km Miejsce Open is uniformly distributed Uniform
Numer startowy has unique values Unique

Reproduction

Analysis started2025-06-29 08:21:06.336154
Analysis finished2025-06-29 08:21:34.687870
Duration28.35 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Miejsce
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct8150
Distinct (%)100.0%
Missing800
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean4075.5
Minimum1
Maximum8150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:34.758870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile408.45
Q12038.25
median4075.5
Q36112.75
95-th percentile7742.55
Maximum8150
Range8149
Interquartile range (IQR)4074.5

Descriptive statistics

Standard deviation2352.8467
Coefficient of variation (CV)0.57731485
Kurtosis-1.2
Mean4075.5
Median Absolute Deviation (MAD)2037.5
Skewness0
Sum33215325
Variance5535887.5
MonotonicityStrictly increasing
2025-06-29T10:21:34.895545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8120 1
 
< 0.1%
23 1
 
< 0.1%
1 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
Other values (8140) 8140
90.9%
(Missing) 800
 
8.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8150 1
< 0.1%
8149 1
< 0.1%
8148 1
< 0.1%
8147 1
< 0.1%
8146 1
< 0.1%
8145 1
< 0.1%
8144 1
< 0.1%
8143 1
< 0.1%
8142 1
< 0.1%
8141 1
< 0.1%

Numer startowy
Real number (ℝ)

Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4758.4517
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:35.023056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile578.45
Q12504.25
median4770.5
Q37010.75
95-th percentile8807.55
Maximum10000
Range9999
Interquartile range (IQR)4506.5

Descriptive statistics

Standard deviation2645.2071
Coefficient of variation (CV)0.5558966
Kurtosis-1.1315022
Mean4758.4517
Median Absolute Deviation (MAD)2253
Skewness-0.0006045164
Sum42588143
Variance6997120.7
MonotonicityNot monotonic
2025-06-29T10:21:35.138053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2773 1
 
< 0.1%
1787 1
 
< 0.1%
3 1
 
< 0.1%
3832 1
 
< 0.1%
416 1
 
< 0.1%
8476 1
 
< 0.1%
6706 1
 
< 0.1%
1636 1
 
< 0.1%
640 1
 
< 0.1%
6243 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
22 1
< 0.1%
33 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%

Imię
Text

Distinct712
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:35.401949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length16
Mean length6.5384358
Min length1

Characters and Unicode

Total characters58519
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique382 ?
Unique (%)4.3%

Sample

1st rowTOMASZ
2nd rowARKADIUSZ
3rd rowKRZYSZTOF
4th rowDAMIAN
5th rowKAMIL
ValueCountFrequency (%)
tomasz 330
 
3.7%
marcin 302
 
3.4%
piotr 294
 
3.3%
michał 270
 
3.0%
paweł 254
 
2.8%
anonimowy 239
 
2.7%
krzysztof 239
 
2.7%
łukasz 227
 
2.5%
maciej 180
 
2.0%
mateusz 170
 
1.9%
Other values (695) 6469
72.1%
2025-06-29T10:21:35.757782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 10504
17.9%
R 4375
 
7.5%
I 4040
 
6.9%
N 3333
 
5.7%
E 3280
 
5.6%
M 3086
 
5.3%
O 2988
 
5.1%
Z 2974
 
5.1%
S 2688
 
4.6%
T 2639
 
4.5%
Other values (31) 18612
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10504
17.9%
R 4375
 
7.5%
I 4040
 
6.9%
N 3333
 
5.7%
E 3280
 
5.6%
M 3086
 
5.3%
O 2988
 
5.1%
Z 2974
 
5.1%
S 2688
 
4.6%
T 2639
 
4.5%
Other values (31) 18612
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10504
17.9%
R 4375
 
7.5%
I 4040
 
6.9%
N 3333
 
5.7%
E 3280
 
5.6%
M 3086
 
5.3%
O 2988
 
5.1%
Z 2974
 
5.1%
S 2688
 
4.6%
T 2639
 
4.5%
Other values (31) 18612
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10504
17.9%
R 4375
 
7.5%
I 4040
 
6.9%
N 3333
 
5.7%
E 3280
 
5.6%
M 3086
 
5.3%
O 2988
 
5.1%
Z 2974
 
5.1%
S 2688
 
4.6%
T 2639
 
4.5%
Other values (31) 18612
31.8%
Distinct6049
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:35.990085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length21
Mean length7.8382123
Min length2

Characters and Unicode

Total characters70152
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4754 ?
Unique (%)53.1%

Sample

1st rowGRYCKO
2nd rowGARDZIELEWSKI
3rd rowHADAS
4th rowDYDUCH
5th rowMAŃKOWSKI
ValueCountFrequency (%)
zawodnik 246
 
2.7%
nowak 35
 
0.4%
wójcik 24
 
0.3%
kowalski 20
 
0.2%
mazur 20
 
0.2%
król 17
 
0.2%
kaczmarek 17
 
0.2%
walczak 16
 
0.2%
wieczorek 15
 
0.2%
nowicki 14
 
0.2%
Other values (6055) 8575
95.3%
2025-06-29T10:21:36.314384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 7857
 
11.2%
A 7736
 
11.0%
I 6394
 
9.1%
S 4876
 
7.0%
O 4558
 
6.5%
Z 3891
 
5.5%
E 3622
 
5.2%
R 3464
 
4.9%
W 3435
 
4.9%
C 3329
 
4.7%
Other values (34) 20990
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 7857
 
11.2%
A 7736
 
11.0%
I 6394
 
9.1%
S 4876
 
7.0%
O 4558
 
6.5%
Z 3891
 
5.5%
E 3622
 
5.2%
R 3464
 
4.9%
W 3435
 
4.9%
C 3329
 
4.7%
Other values (34) 20990
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 7857
 
11.2%
A 7736
 
11.0%
I 6394
 
9.1%
S 4876
 
7.0%
O 4558
 
6.5%
Z 3891
 
5.5%
E 3622
 
5.2%
R 3464
 
4.9%
W 3435
 
4.9%
C 3329
 
4.7%
Other values (34) 20990
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 7857
 
11.2%
A 7736
 
11.0%
I 6394
 
9.1%
S 4876
 
7.0%
O 4558
 
6.5%
Z 3891
 
5.5%
E 3622
 
5.2%
R 3464
 
4.9%
W 3435
 
4.9%
C 3329
 
4.7%
Other values (34) 20990
29.9%

Miasto
Text

Missing 

Distinct1446
Distinct (%)18.4%
Missing1089
Missing (%)12.2%
Memory size70.1 KiB
2025-06-29T10:21:36.589380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length27
Mean length8.0279863
Min length3

Characters and Unicode

Total characters63108
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique845 ?
Unique (%)10.7%

Sample

1st rowWROCŁAW
2nd rowPOZNAŃ
3rd rowKĘPNO
4th rowMIRKÓW
5th rowWROCŁAW
ValueCountFrequency (%)
wrocław 2488
28.8%
warszawa 260
 
3.0%
wroclaw 224
 
2.6%
poznań 173
 
2.0%
kraków 106
 
1.2%
góra 100
 
1.2%
legnica 65
 
0.8%
łódź 59
 
0.7%
oleśnica 56
 
0.6%
opole 56
 
0.6%
Other values (1496) 5042
58.4%
2025-06-29T10:21:37.008924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 8517
13.5%
A 6980
11.1%
O 6061
 
9.6%
R 5130
 
8.1%
C 4902
 
7.8%
I 3482
 
5.5%
Ł 3348
 
5.3%
E 2937
 
4.7%
Z 2612
 
4.1%
N 2167
 
3.4%
Other values (36) 16972
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 8517
13.5%
A 6980
11.1%
O 6061
 
9.6%
R 5130
 
8.1%
C 4902
 
7.8%
I 3482
 
5.5%
Ł 3348
 
5.3%
E 2937
 
4.7%
Z 2612
 
4.1%
N 2167
 
3.4%
Other values (36) 16972
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 8517
13.5%
A 6980
11.1%
O 6061
 
9.6%
R 5130
 
8.1%
C 4902
 
7.8%
I 3482
 
5.5%
Ł 3348
 
5.3%
E 2937
 
4.7%
Z 2612
 
4.1%
N 2167
 
3.4%
Other values (36) 16972
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 8517
13.5%
A 6980
11.1%
O 6061
 
9.6%
R 5130
 
8.1%
C 4902
 
7.8%
I 3482
 
5.5%
Ł 3348
 
5.3%
E 2937
 
4.7%
Z 2612
 
4.1%
N 2167
 
3.4%
Other values (36) 16972
26.9%

Kraj
Categorical

Imbalance  Missing 

Distinct31
Distinct (%)0.4%
Missing800
Missing (%)8.9%
Memory size70.1 KiB
POL
7930 
GER
 
45
UKR
 
30
GBR
 
23
BLR
 
17
Other values (26)
 
105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24450
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st rowPOL
2nd rowPOL
3rd rowPOL
4th rowPOL
5th rowPOL

Common Values

ValueCountFrequency (%)
POL 7930
88.6%
GER 45
 
0.5%
UKR 30
 
0.3%
GBR 23
 
0.3%
BLR 17
 
0.2%
ITA 12
 
0.1%
AUT 12
 
0.1%
FRA 10
 
0.1%
GRE 10
 
0.1%
CZE 8
 
0.1%
Other values (21) 53
 
0.6%
(Missing) 800
 
8.9%

Length

2025-06-29T10:21:37.095612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pol 7930
97.3%
ger 45
 
0.6%
ukr 30
 
0.4%
gbr 23
 
0.3%
blr 17
 
0.2%
ita 12
 
0.1%
aut 12
 
0.1%
fra 10
 
0.1%
gre 10
 
0.1%
cze 8
 
0.1%
Other values (21) 53
 
0.7%

Most occurring characters

ValueCountFrequency (%)
L 7960
32.6%
P 7938
32.5%
O 7936
32.5%
R 143
 
0.6%
E 88
 
0.4%
G 81
 
0.3%
U 60
 
0.2%
B 47
 
0.2%
A 42
 
0.2%
K 32
 
0.1%
Other values (11) 123
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 7960
32.6%
P 7938
32.5%
O 7936
32.5%
R 143
 
0.6%
E 88
 
0.4%
G 81
 
0.3%
U 60
 
0.2%
B 47
 
0.2%
A 42
 
0.2%
K 32
 
0.1%
Other values (11) 123
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 7960
32.6%
P 7938
32.5%
O 7936
32.5%
R 143
 
0.6%
E 88
 
0.4%
G 81
 
0.3%
U 60
 
0.2%
B 47
 
0.2%
A 42
 
0.2%
K 32
 
0.1%
Other values (11) 123
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 7960
32.6%
P 7938
32.5%
O 7936
32.5%
R 143
 
0.6%
E 88
 
0.4%
G 81
 
0.3%
U 60
 
0.2%
B 47
 
0.2%
A 42
 
0.2%
K 32
 
0.1%
Other values (11) 123
 
0.5%

Drużyna
Text

Missing 

Distinct1985
Distinct (%)58.0%
Missing5529
Missing (%)61.8%
Memory size70.1 KiB
2025-06-29T10:21:37.343122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length84
Median length45
Mean length15.312189
Min length1

Characters and Unicode

Total characters52383
Distinct characters107
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1416 ?
Unique (%)41.4%

Sample

1st rowUKS BLIZA WŁADYSŁAWOWO
2nd rowARKADIUSZGARDZIELEWSKI.PL
3rd rowAZS POLITECHNIKA OPOLSKA
4th rowPARKRUN WROCŁAW
5th rowWOSIEK TEAM KS AZS AWF WROCŁAW
ValueCountFrequency (%)
team 495
 
6.4%
wrocław 167
 
2.1%
running 133
 
1.7%
runners 94
 
1.2%
biega 90
 
1.2%
run 86
 
1.1%
kb 85
 
1.1%
klub 57
 
0.7%
w 57
 
0.7%
grupa 55
 
0.7%
Other values (2610) 6468
83.1%
2025-06-29T10:21:37.775262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4800
 
9.2%
4366
 
8.3%
E 3525
 
6.7%
I 3314
 
6.3%
R 2850
 
5.4%
O 2837
 
5.4%
N 2731
 
5.2%
T 2190
 
4.2%
S 2137
 
4.1%
K 1921
 
3.7%
Other values (97) 21712
41.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52383
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4800
 
9.2%
4366
 
8.3%
E 3525
 
6.7%
I 3314
 
6.3%
R 2850
 
5.4%
O 2837
 
5.4%
N 2731
 
5.2%
T 2190
 
4.2%
S 2137
 
4.1%
K 1921
 
3.7%
Other values (97) 21712
41.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52383
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4800
 
9.2%
4366
 
8.3%
E 3525
 
6.7%
I 3314
 
6.3%
R 2850
 
5.4%
O 2837
 
5.4%
N 2731
 
5.2%
T 2190
 
4.2%
S 2137
 
4.1%
K 1921
 
3.7%
Other values (97) 21712
41.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52383
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4800
 
9.2%
4366
 
8.3%
E 3525
 
6.7%
I 3314
 
6.3%
R 2850
 
5.4%
O 2837
 
5.4%
N 2731
 
5.2%
T 2190
 
4.2%
S 2137
 
4.1%
K 1921
 
3.7%
Other values (97) 21712
41.4%

Płeć
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size70.1 KiB
M
6335 
K
2613 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 6335
70.8%
K 2613
29.2%
(Missing) 2
 
< 0.1%

Length

2025-06-29T10:21:37.899843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-29T10:21:37.965353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 6335
70.8%
k 2613
29.2%

Most occurring characters

ValueCountFrequency (%)
M 6335
70.8%
K 2613
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8948
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 6335
70.8%
K 2613
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8948
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 6335
70.8%
K 2613
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8948
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 6335
70.8%
K 2613
29.2%

Płeć Miejsce
Real number (ℝ)

High correlation  Missing 

Distinct5829
Distinct (%)71.5%
Missing800
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean2415.4866
Minimum1
Maximum5829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:38.043354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile204.45
Q11019.25
median2038
Q33791.75
95-th percentile5421.55
Maximum5829
Range5828
Interquartile range (IQR)2772.5

Descriptive statistics

Standard deviation1667.3047
Coefficient of variation (CV)0.69025623
Kurtosis-1.0049142
Mean2415.4866
Median Absolute Deviation (MAD)1264
Skewness0.44902415
Sum19686216
Variance2779904.9
MonotonicityNot monotonic
2025-06-29T10:21:38.156090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2309 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
12 2
 
< 0.1%
13 2
 
< 0.1%
14 2
 
< 0.1%
15 2
 
< 0.1%
Other values (5819) 8130
90.8%
(Missing) 800
 
8.9%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
5829 1
< 0.1%
5828 1
< 0.1%
5827 1
< 0.1%
5826 1
< 0.1%
5825 1
< 0.1%
5824 1
< 0.1%
5823 1
< 0.1%
5822 1
< 0.1%
5821 1
< 0.1%
5820 1
< 0.1%

Kategoria wiekowa
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing11
Missing (%)0.1%
Memory size70.1 KiB
M40
2188 
M30
2032 
M20
1078 
K30
942 
K40
904 
Other values (8)
1795 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26817
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM30
2nd rowM30
3rd rowM20
4th rowM30
5th rowM20

Common Values

ValueCountFrequency (%)
M40 2188
24.4%
M30 2032
22.7%
M20 1078
12.0%
K30 942
10.5%
K40 904
10.1%
M50 694
 
7.8%
K20 493
 
5.5%
M60 286
 
3.2%
K50 220
 
2.5%
K60 48
 
0.5%
Other values (3) 54
 
0.6%

Length

2025-06-29T10:21:38.258091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m40 2188
24.5%
m30 2032
22.7%
m20 1078
12.1%
k30 942
10.5%
k40 904
10.1%
m50 694
 
7.8%
k20 493
 
5.5%
m60 286
 
3.2%
k50 220
 
2.5%
k60 48
 
0.5%
Other values (3) 54
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 8939
33.3%
M 6326
23.6%
4 3092
 
11.5%
3 2974
 
11.1%
K 2613
 
9.7%
2 1571
 
5.9%
5 914
 
3.4%
6 334
 
1.2%
7 52
 
0.2%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26817
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8939
33.3%
M 6326
23.6%
4 3092
 
11.5%
3 2974
 
11.1%
K 2613
 
9.7%
2 1571
 
5.9%
5 914
 
3.4%
6 334
 
1.2%
7 52
 
0.2%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26817
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8939
33.3%
M 6326
23.6%
4 3092
 
11.5%
3 2974
 
11.1%
K 2613
 
9.7%
2 1571
 
5.9%
5 914
 
3.4%
6 334
 
1.2%
7 52
 
0.2%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26817
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8939
33.3%
M 6326
23.6%
4 3092
 
11.5%
3 2974
 
11.1%
K 2613
 
9.7%
2 1571
 
5.9%
5 914
 
3.4%
6 334
 
1.2%
7 52
 
0.2%
8 2
 
< 0.1%

Kategoria wiekowa Miejsce
Real number (ℝ)

High correlation  Missing 

Distinct1987
Distinct (%)24.4%
Missing809
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean649.99263
Minimum1
Maximum1987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:38.347741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37
Q1220
median517
Q3949
95-th percentile1729
Maximum1987
Range1986
Interquartile range (IQR)729

Descriptive statistics

Standard deviation524.04052
Coefficient of variation (CV)0.80622533
Kurtosis-0.34026972
Mean649.99263
Median Absolute Deviation (MAD)332
Skewness0.82893157
Sum5291590
Variance274618.47
MonotonicityNot monotonic
2025-06-29T10:21:38.468745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 13
 
0.1%
1 13
 
0.1%
3 12
 
0.1%
4 12
 
0.1%
5 12
 
0.1%
20 11
 
0.1%
22 11
 
0.1%
21 11
 
0.1%
24 11
 
0.1%
25 11
 
0.1%
Other values (1977) 8024
89.7%
(Missing) 809
 
9.0%
ValueCountFrequency (%)
1 13
0.1%
2 13
0.1%
3 12
0.1%
4 12
0.1%
5 12
0.1%
6 11
0.1%
7 11
0.1%
8 11
0.1%
9 11
0.1%
10 11
0.1%
ValueCountFrequency (%)
1987 1
< 0.1%
1986 1
< 0.1%
1985 1
< 0.1%
1984 1
< 0.1%
1983 1
< 0.1%
1982 1
< 0.1%
1981 1
< 0.1%
1980 1
< 0.1%
1979 1
< 0.1%
1978 1
< 0.1%

Rocznik
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct65
Distinct (%)0.7%
Missing201
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1980.9384
Minimum0
Maximum2006
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:38.615165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1965
Q11977
median1984
Q31991
95-th percentile1999
Maximum2006
Range2006
Interquartile range (IQR)14

Descriptive statistics

Standard deviation71.027734
Coefficient of variation (CV)0.035855599
Kurtosis758.29683
Mean1980.9384
Median Absolute Deviation (MAD)7
Skewness-27.283457
Sum17331230
Variance5044.9389
MonotonicityNot monotonic
2025-06-29T10:21:38.763391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1981 374
 
4.2%
1982 365
 
4.1%
1983 358
 
4.0%
1978 330
 
3.7%
1985 327
 
3.7%
1979 325
 
3.6%
1984 323
 
3.6%
1980 317
 
3.5%
1990 314
 
3.5%
1986 303
 
3.4%
Other values (55) 5413
60.5%
ValueCountFrequency (%)
0 11
0.1%
1934 1
 
< 0.1%
1943 1
 
< 0.1%
1944 1
 
< 0.1%
1946 4
 
< 0.1%
1947 2
 
< 0.1%
1948 2
 
< 0.1%
1949 11
0.1%
1950 5
0.1%
1951 7
0.1%
ValueCountFrequency (%)
2006 3
 
< 0.1%
2005 20
 
0.2%
2004 29
 
0.3%
2003 49
 
0.5%
2002 58
 
0.6%
2001 91
1.0%
2000 136
1.5%
1999 118
1.3%
1998 153
1.7%
1997 186
2.1%

5 km Czas
Date

Missing 

Distinct1150
Distinct (%)14.2%
Missing827
Missing (%)9.2%
Memory size70.1 KiB
Minimum2025-06-29 00:14:37
Maximum2025-06-29 01:03:45
Invalid dates0
Invalid dates (%)0.0%
2025-06-29T10:21:38.900309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:39.029095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

5 km Miejsce Open
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct8123
Distinct (%)100.0%
Missing827
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean4070.6776
Minimum1
Maximum8147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:39.181354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile409.1
Q12035.5
median4071
Q36104.5
95-th percentile7733.9
Maximum8147
Range8146
Interquartile range (IQR)4069

Descriptive statistics

Standard deviation2350.1321
Coefficient of variation (CV)0.57733192
Kurtosis-1.1997943
Mean4070.6776
Median Absolute Deviation (MAD)2035
Skewness0.00059354696
Sum33066114
Variance5523120.9
MonotonicityNot monotonic
2025-06-29T10:21:39.313296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8087 1
 
< 0.1%
47 1
 
< 0.1%
8035 1
 
< 0.1%
8027 1
 
< 0.1%
17 1
 
< 0.1%
8141 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
Other values (8113) 8113
90.6%
(Missing) 827
 
9.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8147 1
< 0.1%
8146 1
< 0.1%
8145 1
< 0.1%
8144 1
< 0.1%
8143 1
< 0.1%
8142 1
< 0.1%
8141 1
< 0.1%
8140 1
< 0.1%
8139 1
< 0.1%
8138 1
< 0.1%

5 km Tempo
Real number (ℝ)

High correlation  Missing 

Distinct1150
Distinct (%)14.2%
Missing827
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean5.4924113
Minimum2.9233333
Maximum12.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:39.563895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.9233333
5-th percentile4.217
Q14.9366667
median5.4466667
Q36.0166667
95-th percentile6.86
Maximum12.75
Range9.8266667
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation0.80753453
Coefficient of variation (CV)0.14702732
Kurtosis1.4468787
Mean5.4924113
Median Absolute Deviation (MAD)0.53666667
Skewness0.41079517
Sum44614.857
Variance0.65211201
MonotonicityNot monotonic
2025-06-29T10:21:39.673771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.48 30
 
0.3%
5.213333333 28
 
0.3%
5.346666667 26
 
0.3%
5.363333333 26
 
0.3%
5.096666667 23
 
0.3%
5.456666667 22
 
0.2%
5.453333333 22
 
0.2%
5.466666667 22
 
0.2%
4.84 22
 
0.2%
4.85 22
 
0.2%
Other values (1140) 7880
88.0%
(Missing) 827
 
9.2%
ValueCountFrequency (%)
2.923333333 1
< 0.1%
2.96 1
< 0.1%
3.123333333 1
< 0.1%
3.153333333 1
< 0.1%
3.23 1
< 0.1%
3.236666667 1
< 0.1%
3.24 1
< 0.1%
3.3 1
< 0.1%
3.313333333 2
< 0.1%
3.376666667 2
< 0.1%
ValueCountFrequency (%)
12.75 1
< 0.1%
11.55666667 1
< 0.1%
10.6 1
< 0.1%
10.50666667 1
< 0.1%
9.06 1
< 0.1%
9.003333333 1
< 0.1%
8.876666667 1
< 0.1%
8.716666667 1
< 0.1%
8.71 1
< 0.1%
8.7 1
< 0.1%

10 km Czas
Date

Missing 

Distinct2109
Distinct (%)25.9%
Missing811
Missing (%)9.1%
Memory size70.1 KiB
Minimum2025-06-29 00:29:15
Maximum2025-06-29 01:36:59
Invalid dates0
Invalid dates (%)0.0%
2025-06-29T10:21:39.793773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:39.954169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

10 km Miejsce Open
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct8139
Distinct (%)100.0%
Missing811
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean4076.5706
Minimum1
Maximum8156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:40.086022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile409.9
Q12038.5
median4076
Q36113.5
95-th percentile7744.1
Maximum8156
Range8155
Interquartile range (IQR)4075

Descriptive statistics

Standard deviation2353.2928
Coefficient of variation (CV)0.57727267
Kurtosis-1.2002086
Mean4076.5706
Median Absolute Deviation (MAD)2038
Skewness0.00045751597
Sum33179208
Variance5537986.9
MonotonicityNot monotonic
2025-06-29T10:21:40.209021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8125 1
 
< 0.1%
38 1
 
< 0.1%
8120 1
 
< 0.1%
8113 1
 
< 0.1%
14 1
 
< 0.1%
8155 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
Other values (8129) 8129
90.8%
(Missing) 811
 
9.1%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8156 1
< 0.1%
8155 1
< 0.1%
8154 1
< 0.1%
8153 1
< 0.1%
8151 1
< 0.1%
8150 1
< 0.1%
8149 1
< 0.1%
8148 1
< 0.1%
8147 1
< 0.1%
8146 1
< 0.1%

10 km Tempo
Real number (ℝ)

High correlation  Missing 

Distinct1288
Distinct (%)15.9%
Missing834
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean5.536863
Minimum2.9266667
Maximum9.7533333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:40.329531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.9266667
5-th percentile4.1766667
Q14.9066667
median5.4566667
Q36.0708333
95-th percentile7.13
Maximum9.7533333
Range6.8266667
Interquartile range (IQR)1.1641667

Descriptive statistics

Standard deviation0.89371636
Coefficient of variation (CV)0.16141204
Kurtosis0.33239033
Mean5.536863
Median Absolute Deviation (MAD)0.575
Skewness0.48701931
Sum44937.18
Variance0.79872893
MonotonicityNot monotonic
2025-06-29T10:21:40.495106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3 24
 
0.3%
5.53 23
 
0.3%
5.483333333 23
 
0.3%
5.373333333 22
 
0.2%
5.64 22
 
0.2%
5.506666667 22
 
0.2%
5.28 22
 
0.2%
5.356666667 21
 
0.2%
5.293333333 21
 
0.2%
5.47 21
 
0.2%
Other values (1278) 7895
88.2%
(Missing) 834
 
9.3%
ValueCountFrequency (%)
2.926666667 1
< 0.1%
2.983333333 1
< 0.1%
3.123333333 1
< 0.1%
3.16 1
< 0.1%
3.196666667 1
< 0.1%
3.27 1
< 0.1%
3.276666667 1
< 0.1%
3.283333333 1
< 0.1%
3.333333333 1
< 0.1%
3.406666667 2
< 0.1%
ValueCountFrequency (%)
9.753333333 1
< 0.1%
9.33 1
< 0.1%
9.266666667 1
< 0.1%
9.263333333 1
< 0.1%
9.253333333 1
< 0.1%
9.13 1
< 0.1%
9.11 1
< 0.1%
9.033333333 1
< 0.1%
9 1
< 0.1%
8.973333333 2
< 0.1%

15 km Czas
Date

Missing 

Distinct2943
Distinct (%)36.2%
Missing809
Missing (%)9.0%
Memory size70.1 KiB
Minimum2025-06-29 00:44:47
Maximum2025-06-29 02:22:41
Invalid dates0
Invalid dates (%)0.0%
2025-06-29T10:21:40.615107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:40.739106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

15 km Miejsce Open
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct8141
Distinct (%)100.0%
Missing809
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean4075.6172
Minimum1
Maximum8153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:40.865104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile410
Q12038
median4075
Q36112
95-th percentile7743
Maximum8153
Range8152
Interquartile range (IQR)4074

Descriptive statistics

Standard deviation2352.3754
Coefficient of variation (CV)0.57718261
Kurtosis-1.1996469
Mean4075.6172
Median Absolute Deviation (MAD)2037
Skewness0.00041963528
Sum33179600
Variance5533670.1
MonotonicityNot monotonic
2025-06-29T10:21:41.008929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8125 1
 
< 0.1%
8126 1
 
< 0.1%
19 1
 
< 0.1%
8153 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
Other values (8131) 8131
90.8%
(Missing) 809
 
9.0%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8153 1
< 0.1%
8151 1
< 0.1%
8150 1
< 0.1%
8148 1
< 0.1%
8147 1
< 0.1%
8146 1
< 0.1%
8145 1
< 0.1%
8144 1
< 0.1%
8143 1
< 0.1%
8142 1
< 0.1%

15 km Tempo
Real number (ℝ)

High correlation  Missing 

Distinct1402
Distinct (%)17.2%
Missing814
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean5.8346624
Minimum3.1066667
Maximum10.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:41.128959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.1066667
5-th percentile4.3533333
Q15.1425
median5.71
Q36.4108333
95-th percentile7.6708333
Maximum10.35
Range7.2433333
Interquartile range (IQR)1.2683333

Descriptive statistics

Standard deviation0.99900066
Coefficient of variation (CV)0.17121824
Kurtosis0.54808445
Mean5.8346624
Median Absolute Deviation (MAD)0.62333333
Skewness0.63999029
Sum47470.813
Variance0.99800231
MonotonicityNot monotonic
2025-06-29T10:21:41.256189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.18 22
 
0.2%
5.606666667 22
 
0.2%
5.29 22
 
0.2%
5.586666667 21
 
0.2%
5.476666667 20
 
0.2%
5.286666667 20
 
0.2%
5.836666667 20
 
0.2%
5.426666667 20
 
0.2%
5.76 20
 
0.2%
5.773333333 19
 
0.2%
Other values (1392) 7930
88.6%
(Missing) 814
 
9.1%
ValueCountFrequency (%)
3.106666667 1
< 0.1%
3.143333333 1
< 0.1%
3.236666667 1
< 0.1%
3.33 1
< 0.1%
3.376666667 1
< 0.1%
3.386666667 1
< 0.1%
3.403333333 1
< 0.1%
3.406666667 1
< 0.1%
3.546666667 1
< 0.1%
3.56 2
< 0.1%
ValueCountFrequency (%)
10.35 1
< 0.1%
10.32666667 1
< 0.1%
10.2 1
< 0.1%
10.17 1
< 0.1%
9.986666667 1
< 0.1%
9.906666667 1
< 0.1%
9.7 1
< 0.1%
9.656666667 1
< 0.1%
9.56 1
< 0.1%
9.543333333 1
< 0.1%

20 km Czas
Date

Missing 

Distinct3703
Distinct (%)45.5%
Missing806
Missing (%)9.0%
Memory size70.1 KiB
Minimum2025-06-29 01:01:43
Maximum2025-06-29 03:17:05
Invalid dates0
Invalid dates (%)0.0%
2025-06-29T10:21:41.385552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:41.562466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

20 km Miejsce Open
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct8144
Distinct (%)100.0%
Missing806
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean4074.7972
Minimum1
Maximum8148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:41.759567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile409.15
Q12038.75
median4074.5
Q36111.25
95-th percentile7739.85
Maximum8148
Range8147
Interquartile range (IQR)4072.5

Descriptive statistics

Standard deviation2351.8252
Coefficient of variation (CV)0.57716374
Kurtosis-1.1998987
Mean4074.7972
Median Absolute Deviation (MAD)2036.5
Skewness-0.00014326581
Sum33185148
Variance5531081.6
MonotonicityNot monotonic
2025-06-29T10:21:41.966561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8128 1
 
< 0.1%
17 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
Other values (8134) 8134
90.9%
(Missing) 806
 
9.0%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8148 1
< 0.1%
8147 1
< 0.1%
8146 1
< 0.1%
8145 1
< 0.1%
8144 1
< 0.1%
8143 1
< 0.1%
8142 1
< 0.1%
8141 1
< 0.1%
8140 1
< 0.1%
8139 1
< 0.1%

20 km Tempo
Real number (ℝ)

High correlation  Missing 

Distinct1685
Distinct (%)20.7%
Missing813
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean6.4817312
Minimum3.3866667
Maximum14.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:42.259679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.3866667
5-th percentile4.7233333
Q15.5966667
median6.26
Q37.1733333
95-th percentile8.9366667
Maximum14.94
Range11.553333
Interquartile range (IQR)1.5766667

Descriptive statistics

Standard deviation1.2758437
Coefficient of variation (CV)0.19683688
Kurtosis1.0440083
Mean6.4817312
Median Absolute Deviation (MAD)0.76
Skewness0.86502339
Sum52741.847
Variance1.6277772
MonotonicityNot monotonic
2025-06-29T10:21:42.395974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.796666667 21
 
0.2%
5.98 20
 
0.2%
6.32 18
 
0.2%
6.26 18
 
0.2%
5.833333333 17
 
0.2%
5.72 17
 
0.2%
5.46 17
 
0.2%
6.036666667 17
 
0.2%
5.853333333 16
 
0.2%
5.48 16
 
0.2%
Other values (1675) 7960
88.9%
(Missing) 813
 
9.1%
ValueCountFrequency (%)
3.386666667 1
 
< 0.1%
3.516666667 1
 
< 0.1%
3.54 1
 
< 0.1%
3.586666667 1
 
< 0.1%
3.59 1
 
< 0.1%
3.616666667 1
 
< 0.1%
3.67 1
 
< 0.1%
3.773333333 1
 
< 0.1%
3.833333333 2
< 0.1%
3.836666667 3
< 0.1%
ValueCountFrequency (%)
14.94 1
< 0.1%
13.47333333 1
< 0.1%
12.67333333 1
< 0.1%
12.25 1
< 0.1%
11.89666667 1
< 0.1%
11.76 1
< 0.1%
11.69666667 1
< 0.1%
11.67333333 1
< 0.1%
11.65333333 1
< 0.1%
11.61 1
< 0.1%

Tempo Stabilność
Real number (ℝ)

High correlation  Missing 

Distinct4667
Distinct (%)57.5%
Missing840
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean0.065680814
Minimum-0.34533333
Maximum0.62953333
Zeros0
Zeros (%)0.0%
Negative161
Negative (%)1.8%
Memory size70.1 KiB
2025-06-29T10:21:42.524959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.34533333
5-th percentile0.01243
Q10.033466667
median0.053066667
Q30.085783333
95-th percentile0.16003667
Maximum0.62953333
Range0.97486667
Interquartile range (IQR)0.052316667

Descriptive statistics

Standard deviation0.049127293
Coefficient of variation (CV)0.74797022
Kurtosis7.435185
Mean0.065680814
Median Absolute Deviation (MAD)0.0234
Skewness1.7087449
Sum532.6714
Variance0.0024134909
MonotonicityNot monotonic
2025-06-29T10:21:42.633959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02853333333 10
 
0.1%
0.0262 9
 
0.1%
0.0504 9
 
0.1%
0.03406666667 8
 
0.1%
0.03906666667 8
 
0.1%
0.03293333333 8
 
0.1%
0.02973333333 8
 
0.1%
0.04506666667 8
 
0.1%
0.0368 8
 
0.1%
0.03973333333 8
 
0.1%
Other values (4657) 8026
89.7%
(Missing) 840
 
9.4%
ValueCountFrequency (%)
-0.3453333333 1
< 0.1%
-0.1176 1
< 0.1%
-0.08306666667 1
< 0.1%
-0.07586666667 1
< 0.1%
-0.0754 1
< 0.1%
-0.0714 1
< 0.1%
-0.06053333333 1
< 0.1%
-0.05806666667 1
< 0.1%
-0.05666666667 1
< 0.1%
-0.05093333333 1
< 0.1%
ValueCountFrequency (%)
0.6295333333 1
< 0.1%
0.5289333333 1
< 0.1%
0.4259333333 1
< 0.1%
0.4052666667 1
< 0.1%
0.3966 1
< 0.1%
0.3951333333 1
< 0.1%
0.3739333333 1
< 0.1%
0.3638 1
< 0.1%
0.3635333333 1
< 0.1%
0.3577333333 1
< 0.1%

Czas
Text

Distinct3800
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:42.880231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.5530726
Min length3

Characters and Unicode

Total characters67600
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1634 ?
Unique (%)18.3%

Sample

1st row01:04:59
2nd row01:06:23
3rd row01:08:24
4th row01:10:16
5th row01:10:27
ValueCountFrequency (%)
dns 762
 
8.5%
dnf 38
 
0.4%
02:01:25 10
 
0.1%
01:57:35 10
 
0.1%
01:49:07 8
 
0.1%
01:51:46 8
 
0.1%
01:56:05 8
 
0.1%
01:54:33 8
 
0.1%
01:58:58 8
 
0.1%
01:43:44 8
 
0.1%
Other values (3790) 8082
90.3%
2025-06-29T10:21:43.309364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 16300
24.1%
0 12663
18.7%
1 8056
11.9%
2 8046
11.9%
5 4807
 
7.1%
4 4610
 
6.8%
3 4217
 
6.2%
9 1686
 
2.5%
8 1653
 
2.4%
7 1607
 
2.4%
Other values (5) 3955
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 16300
24.1%
0 12663
18.7%
1 8056
11.9%
2 8046
11.9%
5 4807
 
7.1%
4 4610
 
6.8%
3 4217
 
6.2%
9 1686
 
2.5%
8 1653
 
2.4%
7 1607
 
2.4%
Other values (5) 3955
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 16300
24.1%
0 12663
18.7%
1 8056
11.9%
2 8046
11.9%
5 4807
 
7.1%
4 4610
 
6.8%
3 4217
 
6.2%
9 1686
 
2.5%
8 1653
 
2.4%
7 1607
 
2.4%
Other values (5) 3955
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 16300
24.1%
0 12663
18.7%
1 8056
11.9%
2 8046
11.9%
5 4807
 
7.1%
4 4610
 
6.8%
3 4217
 
6.2%
9 1686
 
2.5%
8 1653
 
2.4%
7 1607
 
2.4%
Other values (5) 3955
 
5.9%

Tempo
Real number (ℝ)

High correlation  Missing 

Distinct3798
Distinct (%)46.6%
Missing800
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean5.8064089
Minimum3.0805088
Maximum9.7756182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2025-06-29T10:21:43.414365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.0805088
5-th percentile4.360354
Q15.1378684
median5.6901319
Q36.3774986
95-th percentile7.5551473
Maximum9.7756182
Range6.6951094
Interquartile range (IQR)1.2396302

Descriptive statistics

Standard deviation0.96157769
Coefficient of variation (CV)0.16560626
Kurtosis0.2203703
Mean5.8064089
Median Absolute Deviation (MAD)0.61151932
Skewness0.50547422
Sum47322.233
Variance0.92463166
MonotonicityIncreasing
2025-06-29T10:21:43.547088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.755708304 10
 
0.1%
5.573990677 10
 
0.1%
5.430196729 8
 
0.1%
5.172631745 8
 
0.1%
4.917436991 8
 
0.1%
5.298253931 8
 
0.1%
5.639567038 8
 
0.1%
5.50288378 8
 
0.1%
5.102314925 7
 
0.1%
5.12443707 7
 
0.1%
Other values (3788) 8068
90.1%
(Missing) 800
 
8.9%
ValueCountFrequency (%)
3.080508809 1
< 0.1%
3.146875247 1
< 0.1%
3.24247452 1
< 0.1%
3.330963103 1
< 0.1%
3.339653946 1
< 0.1%
3.345184483 1
< 0.1%
3.379947855 1
< 0.1%
3.398909694 1
< 0.1%
3.520581496 1
< 0.1%
3.525321956 1
< 0.1%
ValueCountFrequency (%)
9.775618235 1
< 0.1%
9.64762582 1
< 0.1%
9.593110532 1
< 0.1%
9.572568539 1
< 0.1%
9.566247926 1
< 0.1%
9.556767006 1
< 0.1%
9.499881489 1
< 0.1%
9.491980722 1
< 0.1%
9.249427194 1
< 0.1%
9.180690527 1
< 0.1%

Interactions

2025-06-29T10:21:31.995676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:09.587455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.152869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.622537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:14.227387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:15.911979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:17.515108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:19.547429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.936651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:22.646402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:24.180483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:25.914911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:27.564910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.142698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.505267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.090507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:09.689455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.253974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.723269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:14.330253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.030978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:17.743421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:19.640433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:21.036651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:22.754402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:24.288484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:26.016547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:27.691932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.235660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.597268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.177509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:09.782902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.340180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.815270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:14.459026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.128768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:17.842936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:19.786054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:21.137656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:22.847122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:24.391485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:26.108406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:27.794129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.328312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.679269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.276177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:09.881416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.437183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.911275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:14.614088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.227617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:17.943099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:19.878053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:21.253696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:22.958759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:24.495910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:26.201409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:27.896927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.412957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.769269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.373684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:09.991025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.544185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:13.012353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:14.743480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.332615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:18.052102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:19.995053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:21.356696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.070065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:24.647603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:26.326087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.005927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.507718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.861266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.473480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:10.091535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.647181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:13.114171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:14.847352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.431614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:18.156893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.083055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:21.471735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.171572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:24.771297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:26.438987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.114446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.596460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.949266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.574991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:10.209110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.742848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:13.216225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:14.957681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.556716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:18.258893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.171055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:21.580733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.274514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:24.880185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:26.538988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.225446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.686222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:31.154499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.673033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:10.306945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.829460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:13.335219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:15.054684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.664677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:18.349599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.245811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:21.684736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.361516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:24.988189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:26.626130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.320174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.768223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:31.232707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.795949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:10.410448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.926971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:13.449750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:15.161763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.772911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:18.469600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.338811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:21.788311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.465537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:25.157695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:26.733303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.423173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.862033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:31.327979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.894645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:10.511452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.038972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:13.662895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:15.268766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.880819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:18.730086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.422590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:21.896312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.563048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:25.266001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:26.956944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.523957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.944035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:31.438961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:32.997155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:10.617774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.147972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:13.758898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:15.373883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:16.982508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:18.927334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.510590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:22.011482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.678194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:25.381001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:27.058347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.630955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.039034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:31.559000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:33.090327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:10.740795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.241971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:13.854172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:15.476882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:17.080121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:19.048982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.593590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:22.121992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.793194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:25.480622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:27.158346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.731642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.146617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:31.644000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:33.194948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:10.862942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.352694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:13.952874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:15.588500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:17.197000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:19.211255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.693594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:22.221891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.901485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:25.594571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:27.270978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.847059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.262944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:31.740001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:33.281150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:10.956704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.437986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:14.042386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:15.694165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:17.315511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:19.330255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.768595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:22.438168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:23.992483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:25.707375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:27.365059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:28.954056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.339946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:31.828628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:33.371150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:11.055210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:12.525538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:14.131386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:15.802980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:17.420106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:19.445429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:20.855627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:22.548259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:24.084486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:25.811869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:27.455254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:29.047700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:30.423604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:21:31.909161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-29T10:21:43.653623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
10 km Miejsce Open10 km Tempo15 km Miejsce Open15 km Tempo20 km Miejsce Open20 km Tempo5 km Miejsce Open5 km TempoKategoria wiekowaKategoria wiekowa MiejsceKrajMiejsceNumer startowyPłećPłeć MiejsceRocznikTempoTempo Stabilność
10 km Miejsce Open1.0000.9930.9940.9590.9760.8950.9910.9910.1260.4710.0250.972-0.0050.3390.609-0.0460.9720.411
10 km Tempo0.9931.0000.9950.9710.9820.9130.9690.9690.1580.4730.0000.979-0.0070.3340.614-0.0490.9790.465
15 km Miejsce Open0.9940.9951.0000.9840.9910.9270.9770.9770.1250.4830.0240.988-0.0080.3360.625-0.0440.9880.483
15 km Tempo0.9590.9710.9841.0000.9900.9580.9300.9300.1540.4890.0000.989-0.0110.3200.634-0.0440.9890.595
20 km Miejsce Open0.9760.9820.9910.9901.0000.9670.9530.9530.1210.4990.0150.999-0.0040.3250.645-0.0410.9990.574
20 km Tempo0.8950.9130.9270.9580.9671.0000.8610.8610.1170.5050.0000.9710.0040.2840.649-0.0410.9710.741
5 km Miejsce Open0.9910.9690.9770.9300.9530.8611.0001.0000.1240.4580.0270.948-0.0050.3370.592-0.0410.9480.344
5 km Tempo0.9910.9690.9770.9300.9530.8611.0001.0000.1660.4580.0380.948-0.0050.3180.592-0.0410.9480.344
Kategoria wiekowa0.1260.1580.1250.1540.1210.1170.1240.1661.0000.2610.0220.1210.0150.9990.1911.0000.1560.055
Kategoria wiekowa Miejsce0.4710.4730.4830.4890.4990.5050.4580.4580.2611.0000.0300.5000.0040.4000.7740.0880.5000.310
Kraj0.0250.0000.0240.0000.0150.0000.0270.0380.0220.0301.0000.0080.0280.0000.0230.0000.0000.020
Miejsce0.9720.9790.9880.9890.9990.9710.9480.9480.1210.5000.0081.000-0.0030.3230.647-0.0441.0000.586
Numer startowy-0.005-0.007-0.008-0.011-0.0040.004-0.005-0.0050.0150.0040.028-0.0031.0000.0140.0000.001-0.0030.014
Płeć0.3390.3340.3360.3200.3250.2840.3370.3180.9990.4000.0000.3230.0141.0000.5460.0130.3150.125
Płeć Miejsce0.6090.6140.6250.6340.6450.6490.5920.5920.1910.7740.0230.6470.0000.5461.000-0.0860.6470.412
Rocznik-0.046-0.049-0.044-0.044-0.041-0.041-0.041-0.0411.0000.0880.000-0.0440.0010.013-0.0861.000-0.044-0.041
Tempo0.9720.9790.9880.9890.9990.9710.9480.9480.1560.5000.0001.000-0.0030.3150.647-0.0441.0000.586
Tempo Stabilność0.4110.4650.4830.5950.5740.7410.3440.3440.0550.3100.0200.5860.0140.1250.412-0.0410.5861.000

Missing values

2025-06-29T10:21:33.565337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-29T10:21:33.791846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-29T10:21:34.271913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MiejsceNumer startowyImięNazwiskoMiastoKrajDrużynaPłećPłeć MiejsceKategoria wiekowaKategoria wiekowa MiejsceRocznik5 km Czas5 km Miejsce Open5 km Tempo10 km Czas10 km Miejsce Open10 km Tempo15 km Czas15 km Miejsce Open15 km Tempo20 km Czas20 km Miejsce Open20 km TempoTempo StabilnośćCzasTempo
01.01787TOMASZGRYCKONaNPOLUKS BLIZA WŁADYSŁAWOWOM1.0M301.01992.000:14:371.02.92333300:29:151.02.92666700:44:471.03.10666701:01:431.03.3866670.03140001:04:593.080509
12.03ARKADIUSZGARDZIELEWSKIWROCŁAWPOLARKADIUSZGARDZIELEWSKI.PLM2.0M302.01986.000:14:482.02.96000000:29:432.02.98333300:45:262.03.14333301:03:082.03.5400000.03800001:06:233.146875
23.03832KRZYSZTOFHADASPOZNAŃPOLNaNM3.0M201.01996.000:15:464.03.15333300:31:233.03.12333300:47:343.03.23666701:05:093.03.5166670.02406701:08:243.242475
34.0416DAMIANDYDUCHKĘPNOPOLAZS POLITECHNIKA OPOLSKAM4.0M303.01988.000:16:116.03.23666700:32:105.03.19666700:48:495.03.33000001:06:544.03.6166670.02546701:10:163.330963
45.08476KAMILMAŃKOWSKIMIRKÓWPOLPARKRUN WROCŁAWM5.0M202.01995.000:16:127.03.24000000:32:357.03.27666700:49:317.03.38666701:07:275.03.5866670.02300001:10:273.339654
56.02551ADAMPUTYRAWROCŁAWPOLNaNM6.0M401.01983.000:16:095.03.23000000:32:306.03.27000000:49:316.03.40333301:07:286.03.5900000.02426701:10:343.345184
67.01288MICHAŁWÓJCIKKROŚNICEPOLWOSIEK TEAM KS AZS AWF WROCŁAWM7.0M203.01999.000:15:373.03.12333300:31:254.03.16000000:48:184.03.37666701:07:387.03.8666670.04893301:11:183.379948
78.07837PATRYKCHRZANOWSKIBIELAWAPOLPCH SPORT COMPLEXM8.0M304.01989.000:16:308.03.30000000:32:558.03.28333300:49:578.03.40666701:08:188.03.6700000.02466701:11:423.398910
89.05657CYPRIANGRZELKAPOGRZEBIEŃPOLGKS PIAST GLIWICEM9.0M204.02001.000:17:1014.03.43333300:34:2316.03.44333300:52:1112.03.56000001:11:0310.03.7733330.02273301:14:163.520581
910.05927ADAMKONIECZNYZANIEMYSLPOLNaNM10.0M305.01992.000:16:5311.03.37666700:33:5511.03.40666700:51:3910.03.54666701:10:509.03.8366670.03040001:14:223.525322
MiejsceNumer startowyImięNazwiskoMiastoKrajDrużynaPłećPłeć MiejsceKategoria wiekowaKategoria wiekowa MiejsceRocznik5 km Czas5 km Miejsce Open5 km Tempo10 km Czas10 km Miejsce Open10 km Tempo15 km Czas15 km Miejsce Open15 km Tempo20 km Czas20 km Miejsce Open20 km TempoTempo StabilnośćCzasTempo
8940NaN3857GRZEGORZŁUKASIEWICZNaNNaNStan-mitMNaNM50NaN1967.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
8941NaN1663TOMASZŁUĆNaNNaNNaNMNaNM40NaN1976.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
8942NaN8041JOANNAŁĄCKANaNNaNNaNKNaNK40NaN1977.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
8943NaN8042WIKTORIAŁĄCKANaNNaNNaNKNaNK20NaN2003.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
8944NaN4128ADAMŻELUKNaNNaNNaNMNaNM40NaN1983.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
8945NaN8901KAROLŻUKOWSKINaNNaNOvb PolskaMNaNM30NaN1990.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNFNaN
8946NaN659KORNELIAŻUKROWSKANaNNaNNaNKNaNK30NaN1988.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
8947NaN6402MAGDALENAŻÓRAŃSKANaNNaNIławskie Świry BiegoweKNaNK40NaN1981.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
8948NaN2771KATARZYNAĆWIĄKALSKANaNNaNNaNKNaNK40NaN1982.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
8949NaN2773JULIUSZĆWIĄKALSKINaNNaNNaNMNaNM40NaN1983.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN